In recent years, AI has been integrated in various disciplines with great success (i.e., Supply chain, Recruitment and Video Game Industry). However, one discipline remains heavily apprehensive of AI, and this is the healthcare industry. According to Challen et al (2019), AI and machine learning bots in other industries are able to identify errors and quickly correct themselves before any harm is done. Unfortunately, this is not transferable to the healthcare industry as when it comes to the patient’s health, there is literally no room for trial and error.

Despite the apprehensiveness of the healthcare sector, the use of AI is slowly adopted and can be seen providing simple aid that otherwise requires an additional personnel. Creating chatbots for mental health assistance, monitoring patients’ health and predicting cardiac arrest and/or seizures are some ways AI supports physicians in less high-risk tasks. With the developing technologies in the field of AI, AIs are also able to diagnose patients based on electronic health records, patient history, and pathology images (analysis of blood, urine and tissue samples).

The adoption of AI is slowly progressing due to its ability to alleviate physician burnout, a genuine concern in the field of healthcare. Physician Weekly (2018) states that at least 42% of practitioners experience burnout due to lack of support/personnel, short patient visits, complicated patients and overwhelming workload. Consequently, this affects their performance in providing quality patient care as well as the patient’s safety. Hence, what if the next step for AI in healthcare is to aid physicians with walk-in patients diagnosis and treatment? 

A study by Longoni, Bonezzi & Morewedge (2019) has deduced that despite AI predictive analysis can identify potential ailments faster than human doctors as well as having a higher accuracy rate, patients are reluctant and derive negative utility towards an automated healthcare provider. The argument for these findings are linked to Uniqueness Neglect, a concern that AI are unable to account for each patient’s unique circumstances which makes them hesitant to trust an AI diagnosis. An AI may be able to predict accurately what treatment a patient needs, however, a human practitioner may be able to weigh the pros and cons more specifically, also taking into account the well-being of the patient. 

From a technological point of view, the adoption of AI in the healthcare industry may alleviate physician burnout, aid in less risky decisions such as analytics and image processing, as well as maximizing physician efficiency. However, when it comes to treatment, AI does not have the ability to take over the role of human physicians…… yet.